In the wake of this generation's digital transformation, machine learning and the greater promise of artificial intelligence creates wonder in people's minds and effervescence within organizations. And the attraction to the field is justified: troves of process improvements are announced every day, every new tech startup has an AI play, and even governments are announcing their unique strategies to keep up. The promise of fortunes to come is enough to give vertigo to the most grounded leaders. The break-neck speeds of innovation in AI means that new software ideas can be committed, checked out, trialed, inspected, verified and deployed within a few days. New features go step-by-step with continuous development cycles; new ideas with sudden business opportunities.

It is crucial to prevent algorithms from perpetuating ideologies that disadvantage under-represented groups.

Without change, the scale and frequency of data breaches will continue to increase. To reverse the trend we must better understand how exactly, people contribute to cybersecurity and if and how AI can mitigate these risks. In December 2019, Oracle surveyed 300 enterprise-level IT decision makers nationwide to examine concerns around cybersecurity posture and potential solutions. The survey evaluated the state of cybersecurity preparedness and trust in employing AI to address security gaps at organizations with 500 or more employees. It saw key red flags from patching protocols to workforce constraints that created a cycle of vulnerability and error.

There are many steps in a common machine learning pipeline and much thought that goes into architecting it. There is the problem definition, data acquisition, error detection and data cleaning, etc. In this story, we begin with the assumption that we have a clean and ready to go dataset. With that in mind, we outline the four steps necessary before fitting any machine learning model. We then implement those steps in Pytorch, using a common syntax for invoking multiple method calls; method chaining.

In addition to random celebrity sightings, residents of West Hollywood, CA will soon be spotting autonomous delivery robots in their neighborhood. Last night the West Hollywood city council approved the use of delivery robots on its city streets (hat tip to WeHoVille). A trial of the program will start next month with Postmates' Serve robot and run for 90 days. Serve is a cooler-sized robot that scurries around on four wheels, and while it can run autonomously using sensors and cameras to avoid people and obstacles, the city council is requiring a human chaperone during the trial. Additionally, only three robots can be in operation at once, they can only run during the day, and they aren't allowed on sidewalks deemed substandard.

The US Navy is quietly developing armed robot submarines controlled by onboard artificial intelligence. The vessels could potentially kill without explicit human control. The Office of Naval Research is carrying out the project, known as CLAWS, which it describes in budget documents as an autonomous undersea weapon system for clandestine use. CLAWS will "increase mission areas into kinetic effects", say the documents – military-speak for destroying things.

Back in college I did computer consulting for political consulting firms in the Washington D.C. area. Working on both sides of the political spectrum, I set up CP/M-based PCs (dating myself) on these new things called local area networks. The idea was to provide basic campaign processes, such as direct mail and phone list management, to support state, local, and federal campaigns. The systems in campaign offices are 10,000 times more sophisticated, typically with data moved out to public cloud providers, and advanced data science maximizing the effectiveness of known data for campaigns. The most valuable person in a modern campaign is no longer the campaign manager, but those charged with leveraging the data to effectively target campaign resources.

Even though the first person who wrote an algorithm was a woman in the 19th century, artificial intelligence may be discriminating against women. Two centuries later, algorithms "have the ability to push us back decades" in gender parity, explains Susan Levy, a researcher at University College Dublin who is part of a project to prevent Artificial Intelligence algorithms from learning gender bias. "They can exacerbate toxic masculinity and the attitudes we have been fighting for decades in society," she adds. It algorithms themselves that are at fault, but the history of mankind. Artificial Intelligence (AI) learns from data that is made available, and most of it is biased, says Levy.

The use of artificial intelligence is growing in many areas: hiring, healthcare, travel, household functions. These AI examples rely heavily on deep learning and natural language processing, but its use has sparked a debate about bias and fairness. Human decision making can be shaped by unconscious individual and societal biases. Will AI's decisions be less biased than human ones? How are companies addressing this potential AI bias with regards to hiring, data crunching, and other critical business functions?

CampaignTester was awarded Best Application of Artificial Intelligence to Optimize Creative at the 2020 Campaigns & Elections Reed Awards. CampaignTester is a cutting-edge mobile-based platform that utilizes emotion analytics and machine learning to detect a user's emotion and engagement level while watching video content. Their proprietary platform aims to deliver key audience insights for organizations to validate, revise and perfect their video content messaging. Campaigns & Elections Reed Award winners represent the "best-of-the-best" in the political campaign and advocacy industries. The 2020 Reed Awards honored winners across 16 distinct category groups, representing the different specialisms of the political campaign industry, with distinct category groups for International (non-US) work, and Grassroots Advocacy work.